What is Going On at Kindred Robotics?

Friday, October 28, 2016

There has a growing amount of buzz since D-Wave founder Geordie Rose's latest company emerged from stealth with an audacious patent application. Kindred Systems is working with artificial intelligence and robotics to replicate human intelligence in machines.

A secretive Canadian artificial intelligence company emerging from stealth is rocking the technology world with its list of star-power investors, staff and advisors, and its audacious goal of no less than building machines with human-like intelligence.

According to Kindred's website, "Since its founding in 2014, Kindred has been exploring and engineering systems that enable robots to understand and participate in our world, with the ultimate goal of enabling a future where intelligent machines work together with people to create abundance shared by all."

The vision of Kindred Systems founders Suzanne Gildert and Geordie Rose began at another of Rose's cutting-edge ventures, D-Wave Systems, the quantum computer company. While trying to establish a methodology for training robots, Gildert, who was a physicist at D-Wave came up with the concept of using human control as the basis for the system.

Gildert and Rose quietly left D-Wave in 2014 to found Kindred. “Quantum mechanics is cool, but humanlike intelligence in robots is cooler,” she is reported as saying.

"The opportunity here to build general-purpose machines that have the plethora of capabilities that humans have is unprecedented."

Machine learning has been applied to areas such as image and speech recognition, but machines still struggle to handle basic physical tasks such as picking up and manipulating objects. By improving a robots' physical dexterity with actual human input, Kindred engineers and scientists believe they can dramatically improve robotic performance.

"The light bulb moment was, 'well, a human could supply that training data by moving the robot and if you want good training data you need an immersive situation,"' Gildert told the Chicago Tribune.

Kindred’s central thesis is that human-like intelligence requires a
human-like body. This is known in the artificial intelligence field as embodiment.

Kindred recently filed a U.S. patent application in August this year outlining a system in which an operator wearing a head-mounted display, similar to an Occulus Rift and a robotic exo-suit. Data from the suit and from other external sensors is then analyzed by computers and used to control distant robots.

"Humans and AIs working together to control robots are always better than either by themselves," said Rose,

The patent application goes into some detail about the operator interface, a wearable robotic exo-suit that includes head motion sensors, devices to capture arm movements, and haptic gloves. The operator can also use foot pedals to control the robot’s movement and a virtual reality headset to experience what the robot is seeing.

The suit may even use chemical and biometric sensors, as well as EEGs and MRI devices to capture brain waves.

The surrogate robot is conceived of as a 1.2-meter-tall humanoid, possibly covered with synthetic tactile skin, with two (or more) arms ending in hands or grippers, and wheeled treads for locomotion.

Operator's haptic glove from Kindred's patent application

Cameras on its head would stream high-definition video to its simian operator, while other sensors might include infrared and ultraviolet imaging, GPS, touch, proximity, and strain sensors, and even a radiation detector.

This may sound like just another teleoperation robot, but Kindred's goals are bigger. According to the patent application, “Although a wealth of information included in human brains for performing various human executable tasks is available, robotic-related devices used to execute these tasks have historically not utilized this information or not made good use of it.”

Eventually the system will be able to learn from its operators, and ultimately carry out tasks without a person in the loop. “Device control instructions and environment sensor information generated over…multiple runs may…be used to derive autonomous control information which may be used to facilitate autonomous behavior in an autonomous device,” says the patent application.

Important to note in the patent application and the advisory board members, is the addition of deep learning. The applicaton suggests that Kindred will manage this using “deep hierarchical learning algorithms” such as a conditional deep belief network (CDBN) or a conditional restricted Boltzmann machine (CRBM), a type of powerful recurrent neural network. Artificial intelligence researchers Yoshua Bengio, Rich Sutton, and Graham Taylor are also on the company's advisory board.

In the future, Kindred plans to publish its findings in academic journals or present them at conferences, a common practice AI researchers use to validate their breakthroughs and invite others to build on them. Rose and Gildert spoke this week at the Machine Learning and the Market for Intelligence conference at the University of Toronto, and are increasingly opening the shutters of Kindred's operations and goals.

Kindred is now focusing on bringing its work into the real world through partnerships with industrial robotics companies, Rose told Bloomberg, who declined to discuss specific discussions or agreements. "We saw an opportunity to use that existing base of robots that will be out there in the world in their hundreds of thousands," Gildert said.

Eventually, the company's technology could be used to help make multi-purpose robots that can learn any task, simply by watching how humans do those things.

"The opportunity here to build general-purpose machines that have the plethora of capabilities that humans have is unprecedented," Rose said.